PATTERN RECOGNITION IN COMPUTATIONAL MOLECULAR BIOLOGY: TECHNIQUES AND APPROACHES

I PATTERN RECOGNITION IN SEQUENCES 11 COMBINATORIAL HAPLOTYPING PROBLEMS 3
2 ALGORITHMIC PERSPECTIVES OF THE STRING BARCODING PROBLEMS 28
3 ALIGNMENT-FREE MEASURES FOR WHOLE-GENOME COMPARISON 43
4 A MAXIMUM LIKELIHOOD FRAMEWORK FOR MULTIPLE SEQUENCE LOCAL ALIGNMENT 65
5 GLOBAL SEQUENCE ALIGNMENT WITH A BOUNDED NUMBER OF GAPS 83

II PATTERN RECOGNITION IN SECONDARY STRUCTURES 97
6 A SHORT REVIEW ON PROTEIN SECONDARY STRUCTURE PREDICTION METHODS 99
7 A GENERIC APPROACH TO BIOLOGICAL SEQUENCE SEGMENTATION PROBLEMS: APPLICATION TO PROTEIN SECONDARY STRUCTURE PREDICTION 114
8 STRUCTURAL MOTIF IDENTIFICATION AND RETRIEVAL: A GEOMETRICAL APPROACH 129
9 GENOME-WIDE SEARCH FOR PSEUDOKNOTTED NONCODING RNAs: A COMPARATIVE STUDY 155

III PATTERN RECOGNITION IN TERTIARY STRUCTURES 16510 MOTIF DISCOVERY IN PROTEIN 3D-STRUCTURES USING GRAPH MINING TECHNIQUES 167
11 FUZZY AND UNCERTAIN LEARNING TECHNIQUES FOR THE ANALYSIS AND PREDICTION OF PROTEIN TERTIARY STRUCTURES 190
12 PROTEIN INTER-DOMAIN LINKER PREDICTION 212
13 PREDICTION OF PROLINE CIS–TRANS ISOMERIZATION 236

IV PATTERN RECOGNITION IN QUATERNARY STRUCTURES 24914 PREDICTION OF PROTEIN QUATERNARY STRUCTURES 251
15 COMPARISON OF PROTEIN QUATERNARY STRUCTURES BY GRAPH APPROACHES 266
16 STRUCTURAL DOMAINS IN PREDICTION OF BIOLOGICAL PROTEIN–PROTEIN INTERACTIONS 291

V PATTERN RECOGNITION IN MICROARRAYS 31517 CONTENT-BASED RETRIEVAL OF MICROARRAY EXPERIMENTS 317
18 EXTRACTION OF DIFFERENTIALLY EXPRESSED GENES IN MICROARRAY DATA 335
19 CLUSTERING AND CLASSIFICATION TECHNIQUES FOR GENE EXPRESSION PROFILE PATTERN ANALYSIS 347
20 MINING INFORMATIVE PATTERNS IN MICROARRAY DATA 371
21 ARROW PLOT AND CORRESPONDENCE ANALYSIS MAPS FOR VISUALIZING THE EFFECTS OF BACKGROUND CORRECTION AND NORMALIZATION METHODS ON MICROARRAY DATA 394

VI PATTERN RECOGNITION IN PHYLOGENETIC TREES 41722 PATTERN RECOGNITION IN PHYLOGENETICS: TREES AND NETWORKS 419
23 DIVERSE CONSIDERATIONS FOR SUCCESSFUL PHYLOGENETIC TREE RECONSTRUCTION: IMPACTS FROM MODEL MISSPECIFICATION, RECOMBINATION, HOMOPLASY, AND PATTERN RECOGNITION 439
24 AUTOMATED PLAUSIBILITY ANALYSIS OF LARGE PHYLOGENIES 457
25 A NEW FAST METHOD FOR DETECTING AND VALIDATING HORIZONTAL GENE TRANSFER EVENTS USING PHYLOGENETIC TREES AND AGGREGATION FUNCTIONS 483

VII PATTERN RECOGNITION IN BIOLOGICAL NETWORKS 50526 COMPUTATIONAL METHODS FOR MODELING BIOLOGICAL INTERACTION NETWORKS 507
27 BIOLOGICAL NETWORK INFERENCE AT MULTIPLE SCALES: FROM GENE REGULATION TO SPECIES INTERACTIONS 525
28 DISCOVERING CAUSAL PATTERNS WITH STRUCTURAL EQUATION MODELING: APPLICATION TO TOLL-LIKE RECEPTOR SIGNALING PATHWAY IN CHRONIC LYMPHOCYTIC LEUKEMIA 555
29 ANNOTATING PROTEINS WITH INCOMPLETE LABEL INFORMATION 585
INDEX 609

A comprehensive overview of high-performance pattern recognition techniques and approaches to Computational Molecular Biology
This book surveys the developments of techniques and approaches on pattern recognition related to Computational Molecular Biology. Providing a broad coverage of the field, the authors cover fundamental and technical information on these techniques and approaches, as well as discussing their related problems. The text consists of twenty nine chapters, organized into seven parts: Pattern Recognition in Sequences, Pattern Recognition in Secondary Structures, Pattern Recognition in Tertiary Structures, Pattern Recognition in Quaternary Structures, Pattern Recognition in Microarrays, Pattern Recognition in Phylogenetic Trees, and Pattern Recognition in Biological Networks.

• Surveys the development of techniques and approaches on pattern recognition in biomolecular data
• Discusses pattern recognition in primary, secondary, tertiary and quaternary structures, as well as microarrays, phylogenetic trees and biological networks
• Includes case studies and examples to further illustrate the concepts discussed in the book

Pattern Recognition in Computational Molecular Biology: Techniques and Approaches is a reference for practitioners and professional researches in Computer Science, Life Science, and Mathematics. This book also serves as a supplementary reading for graduate students and young researches interested in Computational Molecular Biology.